One advantage of NAM is that it is possible to find the sufficient context vector needed for inference from table data, even when using a simple unit network [1]}.
NAM-NC repeats the number of variables as many times as the value of time-step \(\tau \) , resulting in a higher computational cost than regular NAM.
Therefore, we sought a way to reduce the computational cost of NAM-NC.
A simple potential solution is parameter sharing, which in multi-task learning is often used to reduce the weight of NNs [2]}.
Hard-sharing, which involves sharing most parameters while independently utilizing only the output layer for multi-task learning, can also significantly reduce weight [3]}.
Thus, we applied hard-sharing in terms of time or features between feature-nets to tie the parameters of NAM-NC.
Its formula, as applied to hard-sharing, is shown in the two-part Eq. REF :
\(\begin{split}\hat{y}^K_{time} =& \beta ^K + w^K_{1,1}f_{:,1}(x_{1,1}) + \cdots + w^K_{\tau ,K}f_{:,K}(x_{\tau ,K}) = \beta ^K + \sum _{t = 1}^{\tau } \sum _{k = 1}^{K} w^K_{t,k}f_{:,k}(x_{t,k}) \\\hat{y}^K_{feature} =& \beta ^K + w^K_{1,1}f_{1,:}(x_{1,1}) + \cdots + w^K_{\tau ,K}f_{\tau ,:}(x_{\tau ,K}) = \beta ^K + \sum _{t = 1}^{\tau } \sum _{k = 1}^{K} w^K_{t,k}f_{t,:}(x_{t,k})\end{split}\) 
